Add Datadog scaler docs for the new option to use the Cluster Agent as proxy

content/docs/2.15/scalers/datadog.md

@@ -13,15 +13,177 @@ polling interval. For more detailed information about polling intervals check
 [the Polling intervals and Datadog rate limiting
 section](#polling-intervals-and-datadog-rate-limiting).
 
+There are two ways to poll Datadog for a query value using the Datadog scaler: using the REST API endpoints, or using the [Datadog Cluster Agent](https://docs.datadoghq.com/containers/cluster_agent/) as proxy. Using the Datadog Cluster Agent as proxy reduces the chance of reaching rate limits. As both types are different in terms of usage and authentication, this documentation handles them separately.
+
+## Using the Datadog Cluster Agent (Experimental)
+
+With this method, the Datadog scaler will be connecting to the Datadog Cluster Agent to retrieve the query values that will be used to drive the KEDA scaling events. This reduces the risk of reaching rate limits for the Datadog API, as the Cluster Agent retrieves metric values in batches.
+
+### Deploy the Datadog Cluster Agent with enabled external metrics
+
+First, deploy the Datadog Cluster Agent enabling the external metrics provider, but without registering it as an `APIService` (to avoid clashing with KEDA).
+
+If you are using Helm to deploy the Cluster Agent, set:
+
+* `clusterAgent.metricsProvider.enabled` to `true`
+* `clusterAgent.metricsProvider.registerAPIService` to `false`
+* `clusterAgent.metricsProvider.useDatadogMetrics` to `true`
+* `clusterAgent.env` to `[{name: DD_EXTERNAL_METRICS_PROVIDER_ENABLE_DATADOGMETRIC_AUTOGEN, value: "false"}]`
+
+If you are using the Datadog Operator, add the following options to your `DatadogAgent` object:
+
+```
+apiVersion: datadoghq.com/v2alpha1
+kind: DatadogAgent
+metadata:
+  name: datadog
+spec:
+  features:
+    externalMetricsServer:
+      enabled: true
+      useDatadogMetrics: true
+      registerAPIService: false
+  override:
+    clusterAgent:
+      env: [{name: DD_EXTERNAL_METRICS_PROVIDER_ENABLE_DATADOGMETRIC_AUTOGEN, value: "false"}]
+[...]
+```
+
+NOTE: Using the Datadog Operator for this purpose requires version 1.8.0 of the operator or later.
+
+### Create a DatadogMetric object for each scaling query
+
+To use the Datadog Cluster Agent to retrieve the query values from Datadog, first, create a [`DatadogMetric`](https://docs.datadoghq.com/containers/guide/cluster_agent_autoscaling_metrics/?tab=helm#create-the-datadogmetric-object) object with the query to drive your scaling events. You will need to add the `external-metrics.datadoghq.com/always-active: "true"` annotation, to ensure the Cluster Agent retrieves the query value. Example:
+
+```yaml
+apiVersion: datadoghq.com/v1alpha1
+kind: DatadogMetric
+metadata:
+  annotations:
+    external-metrics.datadoghq.com/always-active: "true"
+  name: nginx-hits
+spec:
+  query: sum:nginx.net.request_per_s{kube_deployment:nginx}
+```
+
+### Trigger Specification
+
+This specification describes the `datadog` trigger that scales based on a Datadog query, using the Datadog Cluster Agent as proxy.
+
+```yaml
+triggers:
+- type: datadog
+  metricType: Value
+  metadata:
+    useClusterAgentProxy: "true"
+    datadogMetricName: "nginx-hits"
+    datadogMetricNamespace: "default"
+    targetValue: "7.75"
+    activationQueryValue: "1.1"
+    type: "global" # Deprecated in favor of trigger.metricType
+    metricUnavailableValue: "1.5"
+```
+
+**Parameter list:**
+
+- `useClusterAgentProxy` - Whether to use the Cluster Agent as proxy to get the query values. (Values: true, false, Default: false, Optional)
+- `datadogMetricName` - The name of the `DatadogMetric` object to drive the scaling events.
+- `datadogMetricNamespace` - The namespace of the `DatadogMetric` object to drive the scaling events.
+- `targetValue` - Value to reach to start scaling (This value can be a float).
+- `activationQueryValue` - Target value for activating the scaler. Learn more about activation [here](./../concepts/scaling-deployments.md#activating-and-scaling-thresholds).(Default: `0`, Optional, This value can be a float)
+- `type` - Whether to start scaling based on the value or the average between pods. (Values: `average`, `global`, Default:`average`, Optional)
+- `age`: The time window (in seconds) to retrieve metrics from Datadog. (Default: `90`, Optional)
+- `lastAvailablePointOffset`: The offset to retrieve the X to last data point. The value of last data point of some queries might be inaccurate [because of the implicit rollup function](https://docs.datadoghq.com/dashboards/functions/rollup/#rollup-interval-enforced-vs-custom), try to adjust to `1` if you encounter this issue. (Default: `0`, Optional)
+- `metricUnavailableValue`: The value of the metric to return to the HPA if Datadog doesn't find a metric value for the specified time window. If not set, an error will be returned to the HPA, which will log a warning. (Optional, This value can be a float)
+
+> 💡 **NOTE:** The `type` parameter is deprecated in favor of the global `metricType` and will be removed in a future release. Users are advised to use `metricType` instead.
+
+### Authentication
+
+The Datadog scaler with Cluster Agent supports one type of authentication - Bearer authentication.
+
+You can use `TriggerAuthentication` CRD to configure the authentication. Specify `authMode` and other trigger parameters along with secret credentials in `TriggerAuthentication` as mentioned below:
+
+**Common to all authentication types**
+- `authMode` - The authentication mode to connect to the Cluster Agent. (Values: bearer, Default: bearer, Optional)
+- `datadogNamespace` - The namespace where the Datadog Cluster Agent is deployed.
+- `datadogMetricsService` - The service name for the Cluster Agent metrics server. To find the name of the service, check the available services in the Datadog namespace and look for the `*-cluster-agent-metrics*` name pattern.
+- `datadogMetricsServicePort` - The port of the service for the Cluster Agent Metrics API. (Default: 8443, Optional)
+- `unsafeSsl` - Skip certificate validation when connecting over HTTPS. (Values: true, false, Default: false, Optional)
+
+**Bearer authentication:**
+- `token` - The ServiceAccount token to connect to the Datadog Cluster Agent. The service account needs to have permissions to `get`, `watch`, and `list` all `external.metrics.k8s.io` resources.
+
+### Example
+
+```yaml
+apiVersion: v1
+kind: Secret
+metadata:
+  name: datadog-config
+  namespace: my-project
+type: Opaque
+data:
+  datadogNamespace: # Required: base64 encoded value of the namespace where the Datadog Cluster Agent is deployed
+  datadogMetricsService: # Required: base64 encoded value of the Cluster Agent metrics server service
+  unsafeSsl: # Optional: base64 encoded value of `true` or `false`
+  authMode: # Required: base64 encoded value of the authentication mode (in this case, bearer)
+---
+apiVersion: keda.sh/v1alpha1
+kind: TriggerAuthentication
+metadata:
+  name: datadog-cluster-agent-creds
+  namespace: my-project
+spec:
+  secretTargetRef:
+    - parameter: token
+      name: dd-cluster-agent-token
+      key: token
+    - parameter: datadogNamespace
+      name: datadog-config
+      key: namespace
+    - parameter: unsafeSsl
+      name: datadog-config
+      key: unsafeSsl
+    - parameter: authMode
+      name: datadog-config
+      key: authMode
+---
+apiVersion: keda.sh/v1alpha1
+kind: ScaledObject
+metadata:
+  name: datadog-scaledobject
+  namespace: my-project
+spec:
+  scaleTargetRef:
+    name: nginx
+  maxReplicaCount: 3
+  minReplicaCount: 1
+  pollingInterval: 60
+  triggers:
+  - type: datadog
+    metadata:
+      useClusterAgentProxy: "true"
+      datadogMetricName: "nginx-hits"
+      datadogMetricNamespace: "default"
+      targetValue: "2"
+      type: "global"
+    authenticationRef:
+      name: datadog-cluster-agent-creds
+```
+
+## Using the Datadog REST API
+
 ### Trigger Specification
 
-This specification describes the `datadog` trigger that scales based on a Datadog metric.
+This specification describes the `datadog` trigger that scales based on a Datadog query, using the Datadog REST API.
 
 ```yaml
 triggers:
 - type: datadog
   metricType: Value
   metadata:
+    useClusterAgentProxy: "false"
     query: "sum:trace.redis.command.hits{env:none,service:redis}.as_count()"
     queryValue: "7.75"
     activationQueryValue: "1.1"
@@ -35,6 +197,7 @@ triggers:
 
 **Parameter list:**
 
+- `useClusterAgentProxy` - Whether to use the Cluster Agent as proxy to get the query values. (Default: false)
 - `query` - The Datadog query to run.
 - `queryValue` - Value to reach to start scaling (This value can be a float).
 - `activationQueryValue` - Target value for activating the scaler. Learn more about activation [here](./../concepts/scaling-deployments.md#activating-and-scaling-thresholds).(Default: `0`, Optional, This value can be a float)
@@ -124,7 +287,7 @@ spec:
       name: keda-trigger-auth-datadog-secret
 ```
 
-## Polling intervals and Datadog rate limiting
+### Polling intervals and Datadog rate limiting
 
 [API Datadog endpoints are rate
 limited](https://docs.datadoghq.com/api/latest/rate-limits/). Depending on the
@@ -146,14 +309,14 @@ was started with `--horizontal-pod-autoscaler-sync-period=30`, the HPA will poll
 Datadog for a metric value every 30 seconds while the number of replicas is
 between 1 and N.
 
-## Multi-Query Support
+### Multi-Query Support
 
 To reduce issues with API rate limiting from Datadog, it is possible to send a single query, which contains multiple queries, comma-seperated.
 When doing this, the results from each query are aggregated based on the `queryAggregator` value (eg: `max` or `average`).
 
 > 💡 **NOTE:** Because the average/max aggregation operation happens at the scaler level, there won't be any validation or errors if the queries don't make sense to aggregate. Be sure to read and understand the two patterns below before using Multi-Query.
 
-### Example 1 - Aggregating Similar Metrics
+#### Example 1 - Aggregating Similar Metrics
 
 Simple aggregation works well, when wanting to scale on more than one metric with similar return values/scale (ie. where multiple metrics can use a single `queryValue` and still make sense).
 
@@ -188,7 +351,7 @@ The example above looks at the `http.requests` value for a service; taking two v
 
 This works particularly well when scaling against the same metric, but with slightly different parameters, or methods like ```week_before()``` for example.
 
-### Example 2 - Driving scale directly
+#### Example 2 - Driving scale directly
 
 When wanting to scale on non-similar metrics, whilst still benefiting from reduced API calls with multi-query support, the easiest way to do this is to make each query directly return the desired scale (eg: number of pods), and then `max` or `average` the results to get the desired target scale.
 
@@ -224,9 +387,9 @@ spec:
 
 Using the example above, if we assume that `http.requests` is currently returning `360`, dividing that by `180` in the query, results in a value of `2`; if `http.backlog` returns `90`, dividing that by `30` in the query, results in a value of `3`. With the `max` Aggregator set, the scaler will set the target scale to `3` as that is the higher value from all returned queries.
 
-## Cases of unexpected metrics value in DataDog API response
+### Cases of unexpected metrics value in DataDog API response
 
-### Latest data point is unavailable
+#### Latest data point is unavailable
 
 By default, Datadog scaler retrieves the metrics with time window from `now - metadata.age (in seconds)` to `now`, however, some kinds of queries need a small delay (usually 30 secs - 2 mins) before data is available when querying from the API. In this case, adjust `timeWindowOffset` to ensure that the latest point of your query is always available.
 
@@ -256,7 +419,7 @@ spec:
 ```
 Check [here](https://github.com/kedacore/keda/pull/3954#discussion_r1042820206) for the details of this issue
 
-### The value of last data point is inaccurate
+#### The value of last data point is inaccurate
 
 Datadog implicitly rolls up data points automatically with the `avg` method, effectively displaying the average of all data points within a time interval for a given metric. Essentially, there is a rollup for each point. The values at the end attempt to have the rollup applied. When this occurs, it looks at a X second bucket according to your time window, and will default average those values together. Since this is the last point in the query, there are no other values to average with in that X second bucket. This leads to the value of last data point that was not rolled up in the same fashion as the others, and leads to an inaccurate number. In these cases, adjust `lastAvailablePointOffset` to 1 to use the second to last points of an API response would be the most accurate.